### To Do 

# Actual statistics for relationships between temperature and CTmax, size, and fecundity
# Pull residuals from CTmax ~ temperature model, and examine the change over time in lab and the relationship with fecundity

Copepod Collection

Copepods were collected at approximately weekly intervals from Lake Champlain (Burlington Fishing Pier). Plankton was collected from the top 3 meters using a 250 um mesh net. Copepods from 12 collections were used to make a total of 263 thermal limit measurements. Over this time period, collection temperatures ranged from 10.5 to 26.5°C.

Temperature Variation

Water temperatures in Lake Champlain reached a minimum in February. Sampling for this project began during the Spring warming period. Temperature variability (both ranges and variance) increase with temperature, but are strongly affected by the length of time period examined.

# Lake Champlain near Burlington, VT
siteNumber = "04294500"
ChamplainInfo = readNWISsite(siteNumber)
parameterCd = "00010"
startDate = "2023-01-01"
endDate = ""
#statCd = c("00001", "00002","00003", "00011") # 1 - max, 2 - min, 3 = mean

# Constructs the URL for the data wanted then downloads the data
url = constructNWISURL(siteNumbers = siteNumber, parameterCd = parameterCd, 
                       startDate = startDate, endDate = endDate, service = "uv")

temp_data = importWaterML1(url, asDateTime = T) %>% 
  mutate("date" = as.Date(dateTime)) %>% 
  select(date, "temp" = X_00010_00000)


## Daily values
daily_temp_data = temp_data %>%
  ungroup() %>% 
  group_by(date) %>% 
  summarise(mean_temp = mean(temp),
            med_temp = median(temp),
            var_temp = var(temp), 
            min_temp = min(temp), 
            max_temp = max(temp)) %>% 
  mutate("range_temp" = max_temp - min_temp)

daily_plot = daily_temp_data %>% 
  pivot_longer(cols = c(-date),
               names_to = "parameter", 
               values_to = "temp") %>% 
  ggplot(aes(x = date, y = temp, colour = parameter)) + 
  geom_line(linewidth = 1) + 
  scale_colour_manual(values = c(
    "mean_temp" = "olivedrab3",
    "med_temp" = "seagreen3",
    "max_temp" = "tomato",  
    "min_temp" = "dodgerblue",
    "range_temp" = "goldenrod3",
    "var_temp" = "darkgoldenrod1"
  )) + 
  scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) + 
  ggtitle("Daily Values") + 
  labs(y = "Temperature (°C)",
       x = "") + 
  theme_bw(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))
## Defining the function to get predictor values for periods of different lengths
get_predictors = function(daily_values, raw_temp, n_days){
  prefix = str_replace_all(xfun::numbers_to_words(n_days), pattern = " ", replacement = "-")
  
  mean_values = daily_values %>% 
    ungroup() %>% 
    mutate(mean_max = slide_vec(.x = max_temp, .f = mean, .before = n_days, .complete = T),
           mean_min = slide_vec(.x = min_temp, .f = mean, .before = n_days, .complete = T),
           mean_range = slide_vec(.x = range_temp, .f = mean, .before = n_days, .complete = T)) %>% 
    select(date, mean_max, mean_min, mean_range) %>% 
    rename_with( ~ paste(prefix, "day", .x, sep = "_"), .cols = c(-date))
  
  period_values = raw_temp %>% 
    mutate(mean = slide_index_mean(temp, i = date, before = days(n_days), 
                                   na_rm = T),
           max = slide_index_max(temp, i = date, before = days(n_days), 
                                 na_rm = T),
           min = slide_index_min(temp, i = date, before = days(n_days),
                                 na_rm = T),
           med = slide_index_dbl(temp, .i = date, .before = days(n_days), 
                                 na_rm = T, .f = median),
           var = slide_index_dbl(temp, .i = date, .before = days(n_days), 
                                 .f = var),
           range = max - min) %>%  
    select(-temp) %>%  
    distinct() %>% 
    rename_with( ~ paste(prefix, "day", .x, sep = "_"), .cols = c(-date))%>% 
    inner_join(mean_values, by = c("date")) %>%  
    drop_na()
  
  return(period_values)
}
## Getting predictor variables for different periods

### ONE WEEK
week_temps = get_predictors(daily_values = daily_temp_data, 
                            raw_temp = temp_data, 
                            n_days = 7)

week_plot = week_temps %>% 
  pivot_longer(cols = c(-date),
               names_to = "parameter", 
               values_to = "temp") %>% 
  filter(parameter %in% c("seven_day_mean",
                          "seven_day_med",
                          "seven_day_max", 
                          "seven_day_min", 
                          "seven_day_var",
                          "seven_day_range")) %>% 
  mutate(parameter = paste(word(parameter, start = 3, sep = fixed("_")), "_temp", sep = "")) %>% 
  ggplot(aes(x = date, y = temp, colour = parameter)) + 
  geom_line(linewidth = 1) + 
  scale_colour_manual(values = c(
    "mean_temp" = "olivedrab3",
    "med_temp" = "seagreen3",
    "max_temp" = "tomato",  
    "min_temp" = "dodgerblue",
    "range_temp" = "goldenrod3",
    "var_temp" = "darkgoldenrod1"
  )) + 
  scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) + 
  ggtitle("One Week") + 
  labs(y = "Temperature (°C)",
       x = "") + 
  theme_bw(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))


### TWO WEEKS
two_week_temps = get_predictors(daily_values = daily_temp_data, 
                                raw_temp = temp_data, 
                                n_days = 14)

two_week_plot = two_week_temps %>% 
  pivot_longer(cols = c(-date),
               names_to = "parameter", 
               values_to = "temp") %>% 
  filter(parameter %in% c("fourteen_day_mean",
                          "fourteen_day_med",
                          "fourteen_day_max", 
                          "fourteen_day_min", 
                          "fourteen_day_var",
                          "fourteen_day_range")) %>% 
  mutate(parameter = paste(word(parameter, start = 3, sep = fixed("_")), "_temp", sep = "")) %>% 
  ggplot(aes(x = date, y = temp, colour = parameter)) + 
  geom_line(linewidth = 1) + 
  scale_colour_manual(values = c(
    "mean_temp" = "olivedrab3",
    "med_temp" = "seagreen3",
    "max_temp" = "tomato",  
    "min_temp" = "dodgerblue",
    "range_temp" = "goldenrod3",
    "var_temp" = "darkgoldenrod1"
  )) + 
  scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) + 
  ggtitle("Two Weeks") + 
  labs(y = "Temperature (°C)",
       x = "") + 
  theme_bw(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))


### FOUR WEEKS
four_week_temps = get_predictors(daily_values = daily_temp_data, 
                                 raw_temp = temp_data, 
                                 n_days = 28)

four_week_plot = four_week_temps %>% 
  pivot_longer(cols = c(-date),
               names_to = "parameter", 
               values_to = "temp") %>% 
  filter(parameter %in% c("twenty-eight_day_mean",
                          "twenty-eight_day_med",
                          "twenty-eight_day_max", 
                          "twenty-eight_day_min", 
                          "twenty-eight_day_var",
                          "twenty-eight_day_range")) %>% 
  mutate(parameter = paste(word(parameter, start = 3, sep = fixed("_")), "_temp", sep = "")) %>% 
  ggplot(aes(x = date, y = temp, colour = parameter)) + 
  geom_line(linewidth = 1) + 
  scale_colour_manual(values = c(
    "mean_temp" = "olivedrab3",
    "med_temp" = "seagreen3",
    "max_temp" = "tomato",  
    "min_temp" = "dodgerblue",
    "range_temp" = "goldenrod3",
    "var_temp" = "darkgoldenrod1"
  )) + 
  scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) + 
  ggtitle("Four Weeks") + 
  labs(y = "Temperature (°C)",
       x = "") + 
  theme_bw(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))


### EIGHT WEEKS
eight_week_temps = get_predictors(daily_values = daily_temp_data, 
                                  raw_temp = temp_data, 
                                  n_days = 56)

eight_week_plot = eight_week_temps %>% 
  pivot_longer(cols = c(-date),
               names_to = "parameter", 
               values_to = "temp") %>% 
  filter(parameter %in% c("fifty-six_day_mean",
                          "fifty-six_day_med",
                          "fifty-six_day_max", 
                          "fifty-six_day_min", 
                          "fifty-six_day_var",
                          "fifty-six_day_range")) %>% 
  mutate(parameter = paste(word(parameter, start = 3, sep = fixed("_")), "_temp", sep = "")) %>% 
  ggplot(aes(x = date, y = temp, colour = parameter)) + 
  geom_line(linewidth = 1) + 
  scale_colour_manual(values = c(
    "mean_temp" = "olivedrab3",
    "med_temp" = "seagreen3",
    "max_temp" = "tomato",  
    "min_temp" = "dodgerblue",
    "range_temp" = "goldenrod3",
    "var_temp" = "darkgoldenrod1"
  )) + 
  scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) + 
  ggtitle("Eight Weeks") + 
  labs(y = "Temperature (°C)",
       x = "") + 
  theme_bw(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))

ggarrange(daily_plot, week_plot, two_week_plot, four_week_plot, eight_week_plot, 
          common.legend = T, nrow = 1, legend = "bottom")

## Daily values for the period examined by dataset
collection_conditions = temp_data %>%
  ungroup() %>% 
  group_by(date) %>% 
  summarise(mean_temp = mean(temp),
            med_temp = median(temp),
            var_temp = var(temp), 
            min_temp = min(temp), 
            max_temp = max(temp)) %>% 
  mutate("range_temp" = max_temp - min_temp,
         date = as.Date(date)) %>% 
  ungroup() %>%  
  filter(date >= (min(as.Date(full_data$collection_date)) - 7))

## Mean female thermal limits for each species, grouped by collection
species_summaries = full_data %>%  
  #filter(sex == "female") %>% 
  group_by(sp_name, collection_date, collection_temp) %>%  
  summarise("mean_ctmax" = mean(ctmax),
            "sample_size" = n(),
            "ctmax_st_err" = (sd(ctmax) / sqrt(sample_size)),
            "ctmax_var" = var(ctmax), 
            "mean_size" = mean(size),
            "size_st_err" = (sd(size) / sqrt(sample_size)),
            "size_var" = var(size)) %>%  
  ungroup() %>% 
  complete(sp_name, collection_date) %>% 
  arrange(desc(sample_size))

ggplot() + 
  geom_vline(data = unique(select(full_data, collection_date)), 
             aes(xintercept = as.Date(collection_date)),
             colour = "grey90",
             linewidth = 1) + 
  geom_line(data = collection_conditions, 
            aes(x = as.Date(date), y = mean_temp),
            colour = "black", 
            linewidth = 2) + 
  # geom_errorbar(data = species_summaries,
  #               aes(x = as.Date(collection_date),
  #                   ymin = mean_ctmax - ctmax_st_err, ymax = mean_ctmax + ctmax_st_err,
  #                   colour = sp_name),
  #               position = position_dodge(width = 1),
  #               width = 5, linewidth = 1) +
  geom_point(data = species_summaries, 
             aes(x = as.Date(collection_date), y = mean_ctmax, colour = sp_name, size = sample_size)) + 
  scale_colour_manual(values = species_cols) + 
  labs(x = "Date", 
       y = "Temperature (°C)", 
       colour = "Species",
       size = "Sample Size") + 
  theme_matt() + 
  theme(legend.position = "right")


size_timeseries = ggplot() + 
  geom_vline(data = unique(select(full_data, collection_date)), 
             aes(xintercept = as.Date(collection_date)),
             colour = "grey90",
             linewidth = 1) + 
  geom_line(data = collection_conditions, 
            aes(x = as.Date(date), y = mean_temp),
            colour = "black", 
            linewidth = 2) + 
  # geom_errorbar(data = species_summaries,
  #               aes(x = as.Date(collection_date), 
  #                   ymin = mean_ctmax - ctmax_st_err, ymax = mean_ctmax + ctmax_st_err,
  #                   colour = sp_name),
  #               position = position_dodge(width = 1),
  #               width = 5, linewidth = 1) + 
  geom_point(data = species_summaries, 
             aes(x = as.Date(collection_date), y = mean_size * 40, colour = sp_name),
             position = position_dodge(width = 1),
             size = 4) + 
  scale_colour_manual(values = species_cols) + 
  scale_y_continuous(
    name = "Temperature", # Features of the first axis
    sec.axis = sec_axis(~./40, name="Prosome Length (mm)"), # Add a second axis and specify its features
    breaks = c(0,5,10,15,20,25,30)
  ) + 
  labs(x = "Date", 
       y = "Temperature (°C)", 
       colour = "Species") + 
  theme_matt() + 
  theme(legend.position = "right")

#ggarrange(ctmax_timeseries, size_timeseries, common.legend = T, legend = "bottom")
## Combine data, then pull out values for each collection date
date_list = as.Date(unique(full_data$collection_date))

temp_predictors = daily_temp_data %>% 
  full_join(week_temps, by = c("date")) %>% 
  full_join(two_week_temps, by = c("date")) %>% 
  full_join(four_week_temps, by = c("date")) %>% 
  full_join(eight_week_temps, by = c("date")) %>% 
  filter(date %in% date_list)

A set of predictors variables were assembled from the continuous temperature data set based on conditions during the day of collection, the week before collections, and the preceding two, four, and eight week periods. This is a preliminary analysis for now. Shown here are the top three factors. Species with no significant predictor or limited collection date distributions were excluded.

corr_vals = full_data %>%  
  filter(sp_name != "Senecella calanoides") %>%
  filter(sp_name != "Limnocalanus macrurus") %>%
  mutate(collection_date = as.Date(collection_date)) %>% 
  full_join(temp_predictors, join_by(collection_date == date)) %>% 
  pivot_longer(cols = c(collection_temp, mean_temp:tail(names(.), 1)),
               values_to = "value", 
               names_to = "predictor") %>%  
  group_by(sp_name, predictor) %>% 
  summarise(correlation = cor.test(ctmax, value)$estimate,
            p.value = cor.test(ctmax, value)$p.value,
            ci_low = cor.test(ctmax, value)$conf.int[1],
            ci_high = cor.test(ctmax, value)$conf.int[2]) %>% 
  mutate(sig = ifelse(p.value <0.05, "Sig.", "Non Sig."))

corr_vals %>%  
  filter(sig == "Sig.") %>% 
  drop_na(correlation) %>% 
  group_by(sp_name) %>%
  arrange(desc(correlation)) %>% 
  slice_head(n = 3) %>% 
  select("Species" = sp_name, "Predictor" = predictor, "Correlation" = correlation, "P-Value" = p.value) %>% 
  knitr::kable(align = "c")
Species Predictor Correlation P-Value
Epischura lacustris fifty-six_day_max 0.7776250 7.8e-06
Epischura lacustris twenty-eight_day_max 0.7775023 7.8e-06
Epischura lacustris seven_day_mean_max 0.7765511 8.1e-06
Leptodiaptomus minutus fourteen_day_med 0.7089108 0.0e+00
Leptodiaptomus minutus min_temp 0.7082184 0.0e+00
Leptodiaptomus minutus mean_temp 0.7079993 0.0e+00
Skistodiaptomus oregonensis seven_day_max 0.6992135 0.0e+00
Skistodiaptomus oregonensis fourteen_day_max 0.6973601 0.0e+00
Skistodiaptomus oregonensis twenty-eight_day_max 0.6951473 0.0e+00

Trait Variation

ctmax_plot = full_data %>% 
  mutate( #sp_name = str_replace(sp_name, pattern = " ",
    #                              replacement = "\n"),
    sp_name = fct_reorder(sp_name, ctmax, mean)) %>% 
  ggplot(aes(y = sp_name, x = ctmax)) + 
  geom_point(aes(colour= sp_name_sub),
             position = position_dodge(width = 0.3),
             size = 4) + 
  scale_colour_manual(values = species_cols) + 
  xlab(NULL) + 
  labs(y = "",
       x = "CTmax (°C)",
       colour = "Group") + 
  theme_matt() + 
  theme(legend.position = "none")

size_plot = full_data %>% 
  mutate(sp_name = fct_reorder(sp_name, ctmax, mean)) %>% 
  ggplot(aes(y = sp_name, x = size)) + 
  geom_point(aes(colour= sp_name_sub),
             position = position_dodge(width = 0.3),
             size = 4) + 
  scale_colour_manual(values = species_cols) + 
  labs(x = "Prosome Length (mm)",
       y = "", 
       colour = "Group") + 
  guides(color = guide_legend(ncol = 1)) +
  theme_matt(base_size = ) + 
  theme(legend.position = "right",
        axis.text.y = element_blank(),
        plot.margin = margin(0, 0, 0, 0,"cm"))

trait_plot = ctmax_plot + size_plot
trait_plot

full_data %>%  
  drop_na(fecundity) %>%  
  ggplot(aes(x = fecundity, fill = sp_name_sub)) + 
  facet_wrap(.~sp_name_sub, ncol = 1) + 
  geom_histogram(binwidth = 2) + 
  scale_fill_manual(values = species_cols) + 
  labs(x = "Fecundity (# Eggs)") +
  theme_matt_facets() + 
  theme(legend.position = "none")

Variation with temperature

ctmax_temp = ggplot(full_data, aes(x = collection_temp, y = ctmax, colour = sp_name)) + 
  geom_smooth(method = "lm", linewidth = 3) +
  geom_point(size = 3) + 
  labs(x = "Collection Temperature (°C)", 
       y = "CTmax (°C)",
       colour = "Species") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

size_temp = ggplot(filter(full_data, sex != "juvenile"), aes(x = collection_temp, y = size, colour = sp_name)) + 
  geom_smooth(method = "lm", linewidth = 3) +
  geom_point(size = 3) + 
  labs(x = "Collection Temperature (°C)", 
       y = "Length (mm)",
       colour = "Species")  + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

wt_temp = ggplot(full_data, aes(x = collection_temp, y = warming_tol, colour = sp_name)) + 
  geom_smooth(method = "lm", linewidth = 3) +
  geom_point(size = 3) + 
  labs(x = "Collection Temperature (°C)", 
       y = "Warming Tolerance (°C)",
       colour = "Species")  + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

eggs_temp = ggplot(full_data, aes(x = collection_temp, y = fecundity, colour = sp_name)) + 
  geom_smooth(method = "lm", linewidth = 3) +
  geom_point(size = 3) + 
  labs(x = "Collection Temperature (°C)", 
       y = "Fecundity (# Eggs)",
       colour = "Species")  + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ggarrange(ctmax_temp, size_temp, wt_temp, eggs_temp, 
          common.legend = T, legend = "right")

ctmax_temp.model = lm(data = full_data, ctmax ~ collection_temp * sp_name)

knitr::kable(car::Anova(ctmax_temp.model))
Sum Sq Df F value Pr(>F)
collection_temp 224.539478 1 178.7447772 0.0000000
sp_name 1174.077704 6 155.7708693 0.0000000
collection_temp:sp_name 2.204612 3 0.5849942 0.6253559
Residuals 316.562807 252 NA NA

ctmax_resids = cbind(full_data, "resids" = ctmax_temp.model$residuals)
ggplot(ctmax_resids, aes(x = days_in_lab, y = resids, colour = sp_name)) + 
  facet_wrap(sp_name~.) + 
  geom_point(size = 4) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  scale_x_continuous(breaks = c(0:3)) + 
  labs(x = "Days in lab", 
       y = "CTmax Residuals") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt_facets() + 
  theme(legend.position = "none")

Given the long generation times of these copepods, decreases in trait variance may indicate selection over the seasonal cycle. Shown below are the variance in observed CTmax and size, plotted against collection date. Variance decreases in Skistodiaptomus, but this pattern is driven by a single collection with high variance early in the year. Size variance increases slightly in Skistodiaptomus. Variance in both CTmax and size is fairly constant in Leptodiaptomus minutus, the only other species collected across the entire set of samples thus far.

ggplot(drop_na(species_summaries, ctmax_var), aes(x = as.Date(collection_date), y = ctmax_var, colour = sp_name)) + 
  facet_wrap(sp_name~.) + 
  geom_point(size = 2) + 
  geom_smooth(method = "lm", se = F) + 
  labs(x = "Collection Temp. (°C)", 
       y = "CTmax Variance") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt_facets() + 
  theme(legend.position = "none")


ggplot(drop_na(species_summaries, size_var), aes(x = as.Date(collection_date), y = size_var, colour = sp_name)) + 
  facet_wrap(sp_name~.) + 
  geom_point(size = 2) + 
  geom_smooth(method = "lm", se = F) + 
  labs(x = "Collection Temp. (°C)", 
       y = "Size Variance") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt_facets() + 
  theme(legend.position = "none")

Sex and stage variation in thermal limits

Previous sections have generally lumped juvenile, female, and male individuals together. There may be important stage- or sex-specific differences in CTmax though. For several species, we have measurements for individuals in different stages or of different sexes.

sex_sample_sizes = ctmax_resids %>%  
  group_by(sp_name, sex) %>%  
  summarise(num = n()) %>%  
  pivot_wider(id_cols = sp_name,
              names_from = sex, 
              values_from = num,
              values_fill = 0) %>% 
  select("Species" = sp_name, "Juvenile" = juvenile, "Female" = female, "Male" = male)

knitr::kable(sex_sample_sizes, align = "c")
Species Juvenile Female Male
Epischura lacustris 9 8 7
Leptodiaptomus minutus 5 94 27
Leptodiaptomus sicilis 0 10 0
Leptodora kindti 1 0 11
Limnocalanus macrurus 2 4 1
Senecella calanoides 0 1 0
Skistodiaptomus oregonensis 2 65 16

The female-male and female-juvenile comparisons show that there are generally no differences in thermal limits between these groups.

ctmax_resids %>% 
  filter(sp_name %in% filter(sex_sample_sizes, Male > 0, Female > 0)$Species & 
           sex != "juvenile") %>% 
  ggplot(aes(x = sex, y = resids, colour = sp_name, group = sp_name)) + 
  facet_wrap(sp_name~., ncol = 2) + 
  geom_smooth(method = "lm", se = F, linewidth = 1) + 
  geom_point(size = 3,
             alpha = 0.5,
             position = position_jitter(height = 0, width = 0.05)) +  
  labs(x = "Sex", 
       y = "CTmax Residuals") + 
  scale_colour_manual(values = species_cols) + 
  theme_bw(base_size = 18) + 
  theme(legend.position = "none", 
        panel.grid = element_blank())

ctmax_resids %>% 
  filter(sp_name %in% filter(sex_sample_sizes, Juvenile > 0 & Female > 0)$Species & 
           sex != "male") %>% 
  ggplot(aes(x = sex, y = resids, colour = sp_name, group = sp_name)) + 
  facet_wrap(sp_name~., ncol = 1) + 
  geom_smooth(method = "lm", se = F, linewidth = 1) + 
  geom_point(size = 3,
             alpha = 0.5,
             position = position_jitter(height = 0, width = 0.05)) +  
  labs(x = "Sex", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = species_cols) + 
  theme_bw(base_size = 18) + 
  theme(legend.position = "none", 
        panel.grid = element_blank())

Trait Correlations

ggplot(full_data, aes(x = size, y = ctmax, colour = sp_name)) + 
  geom_smooth(data = full_data, 
              aes(x = size, y = ctmax),
              method = "lm", 
              colour ="black", 
              linewidth = 2.5) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  geom_point(size = 4) + 
  labs(x = "Length (mm)", 
       y = "CTmax (°C)",
       colour = "Species") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ggplot(ctmax_resids, aes(x = size, y = fecundity, colour = sp_name)) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  geom_point(size = 4) + 
  labs(x = "Prosome length (mm)", 
       y = "Fecundity (# Eggs)",
       colour = "Species") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ggplot(ctmax_resids, aes(x = ctmax, y = fecundity, colour = sp_name)) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  geom_point(size = 4) + 
  labs(x = "CTmax (°C)", 
       y = "Fecundity (# Eggs)") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

---
title: Seasonality in Lake Champlain Copepod Thermal Limits
date: "`r Sys.Date()`"
output: 
  html_document:
          code_folding: hide
          code_download: true
          toc: true
          toc_float: true
  github_document:
          html_preview: false
          toc: true
          toc_depth: 3
---

```{r to-do}
### To Do 

# Actual statistics for relationships between temperature and CTmax, size, and fecundity
# Pull residuals from CTmax ~ temperature model, and examine the change over time in lab and the relationship with fecundity

```


```{r setup, include=T, message = F, warning = F, echo = F}
knitr::opts_chunk$set(
  echo = knitr::is_html_output(),
  fig.align = "center",
  fig.path = "../Figures/markdown/",
  dev = c("png", "pdf"),
  message = FALSE,
  warning = FALSE,
  collapse = T
)

theme_matt = function(base_size = 18,
                      dark_text = "grey20"){
  mid_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[2]
  light_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[3]
  
  ggpubr::theme_pubr(base_family="sans") %+replace% 
    theme(
      panel.background  = element_rect(fill="transparent", colour=NA), 
      plot.background = element_rect(fill="transparent", colour=NA), 
      legend.background = element_rect(fill="transparent", colour=NA),
      legend.key = element_rect(fill="transparent", colour=NA),
      text = element_text(colour = mid_text, lineheight = 1.1),
      title = element_text(size = base_size * 1.5,
                           colour = dark_text),
      axis.text = element_text(size = base_size,
                               colour = mid_text),
      axis.title.x = element_text(size = base_size * 1.2,
                                  margin = unit(c(3, 0, 0, 0), "mm")),
      axis.title.y = element_text(size = base_size * 1.2,
                                  margin = unit(c(0, 5, 0, 0), "mm"), 
                                  angle = 90),
      legend.text = element_text(size=base_size * 0.9),
      legend.title = element_text(size = base_size * 0.9, 
                                  face = "bold"),
      plot.margin = margin(0.25, 0.25, 0.25, 0.25,"cm")
    )
}

theme_matt_facets = function(base_size = 18,
                             dark_text = "grey20"){
  mid_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[2]
  light_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[3]
  
  theme_bw(base_family="sans") %+replace% 
    theme(
      panel.grid = element_blank(),
      panel.background  = element_rect(fill="transparent", colour=NA), 
      plot.background = element_rect(fill="transparent", colour=NA), 
      legend.background = element_rect(fill="transparent", colour=NA),
      legend.key = element_rect(fill="transparent", colour=NA),
      text = element_text(colour = mid_text, lineheight = 1.1),
      strip.text.x = element_text(size = base_size),
      title = element_text(size = base_size * 1.5,
                           colour = dark_text),
      axis.text = element_text(size = base_size,
                               colour = mid_text),
      axis.title.x = element_text(size = base_size * 1.2,
                                  margin = unit(c(3, 0, 0, 0), "mm")),
      axis.title.y = element_text(size = base_size * 1.2,
                                  margin = unit(c(0, 5, 0, 0), "mm"), 
                                  angle = 90),
      legend.text = element_text(size=base_size * 0.9),
      legend.title = element_text(size = base_size * 0.9, 
                                  face = "bold"),
      plot.margin = margin(0.25, 0.25, 0.25, 0.25,"cm")
    )
}

species_cols = c("Leptodiaptomus minutus" = "#ffd029",
                 "Leptodiaptomus minutus juvenile" = "#e3d8af",
                 "Leptodiaptomus minutus male" = "#ffe896",
                 "Leptodiaptomus sicilis" = "#D86F29",
                 "Leptodiaptomus sicilis male" = "#E28C00",
                 "Skistodiaptomus oregonensis" = "#C5C35A",
                 "Skistodiaptomus oregonensis male" = "#e6e6aa", 
                 "Epischura lacustris juvenile" = "plum1", 
                 "Epischura lacustris male" = "plum3", 
                 "Epischura lacustris" = "plum4", 
                 "Limnocalanus macrurus" = "skyblue4", 
                 "Limnocalanus macrurus male" = "skyblue3", 
                 "Limnocalanus macrurus juvenile" = "skyblue", 
                 "Senecella calanoides" = "darkseagreen3",
                 "Leptodora kindti male" = "lightblue3",
                 "Leptodora kindti" = "lightblue4",
                 "Leptodora kindti juvenile" = "lightblue")
```

## Copepod Collection

Copepods were collected at approximately weekly intervals from Lake Champlain (Burlington Fishing Pier). Plankton was collected from the top 3 meters using a 250 um mesh net. Copepods from `r length(unique(full_data$collection_date))` collections were used to make a total of `r dim(full_data)[1]` thermal limit measurements. Over this time period, collection temperatures ranged from `r paste(min(full_data$collection_temp), " to ", max(full_data$collection_temp), sep = "")`°C. 

## Temperature Variation 
Water temperatures in Lake Champlain reached a minimum in February. Sampling for this project began during the Spring warming period. Temperature variability (both ranges and variance) increase with temperature, but are strongly affected by the length of time period examined.    

```{r pulling-temp-data}
# Lake Champlain near Burlington, VT
siteNumber = "04294500"
ChamplainInfo = readNWISsite(siteNumber)
parameterCd = "00010"
startDate = "2023-01-01"
endDate = ""
#statCd = c("00001", "00002","00003", "00011") # 1 - max, 2 - min, 3 = mean

# Constructs the URL for the data wanted then downloads the data
url = constructNWISURL(siteNumbers = siteNumber, parameterCd = parameterCd, 
                       startDate = startDate, endDate = endDate, service = "uv")

temp_data = importWaterML1(url, asDateTime = T) %>% 
  mutate("date" = as.Date(dateTime)) %>% 
  select(date, "temp" = X_00010_00000)


## Daily values
daily_temp_data = temp_data %>%
  ungroup() %>% 
  group_by(date) %>% 
  summarise(mean_temp = mean(temp),
            med_temp = median(temp),
            var_temp = var(temp), 
            min_temp = min(temp), 
            max_temp = max(temp)) %>% 
  mutate("range_temp" = max_temp - min_temp)

daily_plot = daily_temp_data %>% 
  pivot_longer(cols = c(-date),
               names_to = "parameter", 
               values_to = "temp") %>% 
  ggplot(aes(x = date, y = temp, colour = parameter)) + 
  geom_line(linewidth = 1) + 
  scale_colour_manual(values = c(
    "mean_temp" = "olivedrab3",
    "med_temp" = "seagreen3",
    "max_temp" = "tomato",  
    "min_temp" = "dodgerblue",
    "range_temp" = "goldenrod3",
    "var_temp" = "darkgoldenrod1"
  )) + 
  scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) + 
  ggtitle("Daily Values") + 
  labs(y = "Temperature (°C)",
       x = "") + 
  theme_bw(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))
```

```{r predictors-function}
## Defining the function to get predictor values for periods of different lengths
get_predictors = function(daily_values, raw_temp, n_days){
  prefix = str_replace_all(xfun::numbers_to_words(n_days), pattern = " ", replacement = "-")
  
  mean_values = daily_values %>% 
    ungroup() %>% 
    mutate(mean_max = slide_vec(.x = max_temp, .f = mean, .before = n_days, .complete = T),
           mean_min = slide_vec(.x = min_temp, .f = mean, .before = n_days, .complete = T),
           mean_range = slide_vec(.x = range_temp, .f = mean, .before = n_days, .complete = T)) %>% 
    select(date, mean_max, mean_min, mean_range) %>% 
    rename_with( ~ paste(prefix, "day", .x, sep = "_"), .cols = c(-date))
  
  period_values = raw_temp %>% 
    mutate(mean = slide_index_mean(temp, i = date, before = days(n_days), 
                                   na_rm = T),
           max = slide_index_max(temp, i = date, before = days(n_days), 
                                 na_rm = T),
           min = slide_index_min(temp, i = date, before = days(n_days),
                                 na_rm = T),
           med = slide_index_dbl(temp, .i = date, .before = days(n_days), 
                                 na_rm = T, .f = median),
           var = slide_index_dbl(temp, .i = date, .before = days(n_days), 
                                 .f = var),
           range = max - min) %>%  
    select(-temp) %>%  
    distinct() %>% 
    rename_with( ~ paste(prefix, "day", .x, sep = "_"), .cols = c(-date))%>% 
    inner_join(mean_values, by = c("date")) %>%  
    drop_na()
  
  return(period_values)
}
```


```{r predictors-and-plots, fig.width=12, fig.height=5}
## Getting predictor variables for different periods

### ONE WEEK
week_temps = get_predictors(daily_values = daily_temp_data, 
                            raw_temp = temp_data, 
                            n_days = 7)

week_plot = week_temps %>% 
  pivot_longer(cols = c(-date),
               names_to = "parameter", 
               values_to = "temp") %>% 
  filter(parameter %in% c("seven_day_mean",
                          "seven_day_med",
                          "seven_day_max", 
                          "seven_day_min", 
                          "seven_day_var",
                          "seven_day_range")) %>% 
  mutate(parameter = paste(word(parameter, start = 3, sep = fixed("_")), "_temp", sep = "")) %>% 
  ggplot(aes(x = date, y = temp, colour = parameter)) + 
  geom_line(linewidth = 1) + 
  scale_colour_manual(values = c(
    "mean_temp" = "olivedrab3",
    "med_temp" = "seagreen3",
    "max_temp" = "tomato",  
    "min_temp" = "dodgerblue",
    "range_temp" = "goldenrod3",
    "var_temp" = "darkgoldenrod1"
  )) + 
  scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) + 
  ggtitle("One Week") + 
  labs(y = "Temperature (°C)",
       x = "") + 
  theme_bw(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))


### TWO WEEKS
two_week_temps = get_predictors(daily_values = daily_temp_data, 
                                raw_temp = temp_data, 
                                n_days = 14)

two_week_plot = two_week_temps %>% 
  pivot_longer(cols = c(-date),
               names_to = "parameter", 
               values_to = "temp") %>% 
  filter(parameter %in% c("fourteen_day_mean",
                          "fourteen_day_med",
                          "fourteen_day_max", 
                          "fourteen_day_min", 
                          "fourteen_day_var",
                          "fourteen_day_range")) %>% 
  mutate(parameter = paste(word(parameter, start = 3, sep = fixed("_")), "_temp", sep = "")) %>% 
  ggplot(aes(x = date, y = temp, colour = parameter)) + 
  geom_line(linewidth = 1) + 
  scale_colour_manual(values = c(
    "mean_temp" = "olivedrab3",
    "med_temp" = "seagreen3",
    "max_temp" = "tomato",  
    "min_temp" = "dodgerblue",
    "range_temp" = "goldenrod3",
    "var_temp" = "darkgoldenrod1"
  )) + 
  scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) + 
  ggtitle("Two Weeks") + 
  labs(y = "Temperature (°C)",
       x = "") + 
  theme_bw(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))


### FOUR WEEKS
four_week_temps = get_predictors(daily_values = daily_temp_data, 
                                 raw_temp = temp_data, 
                                 n_days = 28)

four_week_plot = four_week_temps %>% 
  pivot_longer(cols = c(-date),
               names_to = "parameter", 
               values_to = "temp") %>% 
  filter(parameter %in% c("twenty-eight_day_mean",
                          "twenty-eight_day_med",
                          "twenty-eight_day_max", 
                          "twenty-eight_day_min", 
                          "twenty-eight_day_var",
                          "twenty-eight_day_range")) %>% 
  mutate(parameter = paste(word(parameter, start = 3, sep = fixed("_")), "_temp", sep = "")) %>% 
  ggplot(aes(x = date, y = temp, colour = parameter)) + 
  geom_line(linewidth = 1) + 
  scale_colour_manual(values = c(
    "mean_temp" = "olivedrab3",
    "med_temp" = "seagreen3",
    "max_temp" = "tomato",  
    "min_temp" = "dodgerblue",
    "range_temp" = "goldenrod3",
    "var_temp" = "darkgoldenrod1"
  )) + 
  scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) + 
  ggtitle("Four Weeks") + 
  labs(y = "Temperature (°C)",
       x = "") + 
  theme_bw(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))


### EIGHT WEEKS
eight_week_temps = get_predictors(daily_values = daily_temp_data, 
                                  raw_temp = temp_data, 
                                  n_days = 56)

eight_week_plot = eight_week_temps %>% 
  pivot_longer(cols = c(-date),
               names_to = "parameter", 
               values_to = "temp") %>% 
  filter(parameter %in% c("fifty-six_day_mean",
                          "fifty-six_day_med",
                          "fifty-six_day_max", 
                          "fifty-six_day_min", 
                          "fifty-six_day_var",
                          "fifty-six_day_range")) %>% 
  mutate(parameter = paste(word(parameter, start = 3, sep = fixed("_")), "_temp", sep = "")) %>% 
  ggplot(aes(x = date, y = temp, colour = parameter)) + 
  geom_line(linewidth = 1) + 
  scale_colour_manual(values = c(
    "mean_temp" = "olivedrab3",
    "med_temp" = "seagreen3",
    "max_temp" = "tomato",  
    "min_temp" = "dodgerblue",
    "range_temp" = "goldenrod3",
    "var_temp" = "darkgoldenrod1"
  )) + 
  scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) + 
  ggtitle("Eight Weeks") + 
  labs(y = "Temperature (°C)",
       x = "") + 
  theme_bw(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))

ggarrange(daily_plot, week_plot, two_week_plot, four_week_plot, eight_week_plot, 
          common.legend = T, nrow = 1, legend = "bottom")
```

```{r ctmax-timeseries, fig.width=10, fig.height=5}
## Daily values for the period examined by dataset
collection_conditions = temp_data %>%
  ungroup() %>% 
  group_by(date) %>% 
  summarise(mean_temp = mean(temp),
            med_temp = median(temp),
            var_temp = var(temp), 
            min_temp = min(temp), 
            max_temp = max(temp)) %>% 
  mutate("range_temp" = max_temp - min_temp,
         date = as.Date(date)) %>% 
  ungroup() %>%  
  filter(date >= (min(as.Date(full_data$collection_date)) - 7))

## Mean female thermal limits for each species, grouped by collection
species_summaries = full_data %>%  
  #filter(sex == "female") %>% 
  group_by(sp_name, collection_date, collection_temp) %>%  
  summarise("mean_ctmax" = mean(ctmax),
            "sample_size" = n(),
            "ctmax_st_err" = (sd(ctmax) / sqrt(sample_size)),
            "ctmax_var" = var(ctmax), 
            "mean_size" = mean(size),
            "size_st_err" = (sd(size) / sqrt(sample_size)),
            "size_var" = var(size)) %>%  
  ungroup() %>% 
  complete(sp_name, collection_date) %>% 
  arrange(desc(sample_size))

ggplot() + 
  geom_vline(data = unique(select(full_data, collection_date)), 
             aes(xintercept = as.Date(collection_date)),
             colour = "grey90",
             linewidth = 1) + 
  geom_line(data = collection_conditions, 
            aes(x = as.Date(date), y = mean_temp),
            colour = "black", 
            linewidth = 2) + 
  # geom_errorbar(data = species_summaries,
  #               aes(x = as.Date(collection_date),
  #                   ymin = mean_ctmax - ctmax_st_err, ymax = mean_ctmax + ctmax_st_err,
  #                   colour = sp_name),
  #               position = position_dodge(width = 1),
  #               width = 5, linewidth = 1) +
  geom_point(data = species_summaries, 
             aes(x = as.Date(collection_date), y = mean_ctmax, colour = sp_name, size = sample_size)) + 
  scale_colour_manual(values = species_cols) + 
  labs(x = "Date", 
       y = "Temperature (°C)", 
       colour = "Species",
       size = "Sample Size") + 
  theme_matt() + 
  theme(legend.position = "right")

size_timeseries = ggplot() + 
  geom_vline(data = unique(select(full_data, collection_date)), 
             aes(xintercept = as.Date(collection_date)),
             colour = "grey90",
             linewidth = 1) + 
  geom_line(data = collection_conditions, 
            aes(x = as.Date(date), y = mean_temp),
            colour = "black", 
            linewidth = 2) + 
  # geom_errorbar(data = species_summaries,
  #               aes(x = as.Date(collection_date), 
  #                   ymin = mean_ctmax - ctmax_st_err, ymax = mean_ctmax + ctmax_st_err,
  #                   colour = sp_name),
  #               position = position_dodge(width = 1),
  #               width = 5, linewidth = 1) + 
  geom_point(data = species_summaries, 
             aes(x = as.Date(collection_date), y = mean_size * 40, colour = sp_name),
             position = position_dodge(width = 1),
             size = 4) + 
  scale_colour_manual(values = species_cols) + 
  scale_y_continuous(
    name = "Temperature", # Features of the first axis
    sec.axis = sec_axis(~./40, name="Prosome Length (mm)"), # Add a second axis and specify its features
    breaks = c(0,5,10,15,20,25,30)
  ) + 
  labs(x = "Date", 
       y = "Temperature (°C)", 
       colour = "Species") + 
  theme_matt() + 
  theme(legend.position = "right")

#ggarrange(ctmax_timeseries, size_timeseries, common.legend = T, legend = "bottom")
```


```{r pulling-predictors}
## Combine data, then pull out values for each collection date
date_list = as.Date(unique(full_data$collection_date))

temp_predictors = daily_temp_data %>% 
  full_join(week_temps, by = c("date")) %>% 
  full_join(two_week_temps, by = c("date")) %>% 
  full_join(four_week_temps, by = c("date")) %>% 
  full_join(eight_week_temps, by = c("date")) %>% 
  filter(date %in% date_list)
```

A set of predictors variables were assembled from the continuous temperature data set based on conditions during the day of collection, the week before collections, and the preceding two, four, and eight week periods. This is a preliminary analysis for now. Shown here are the top three factors. Species with no significant predictor or limited collection date distributions were excluded. 

```{r predictor-correlations}
corr_vals = full_data %>%  
  filter(sp_name != "Senecella calanoides") %>%
  filter(sp_name != "Limnocalanus macrurus") %>%
  mutate(collection_date = as.Date(collection_date)) %>% 
  full_join(temp_predictors, join_by(collection_date == date)) %>% 
  pivot_longer(cols = c(collection_temp, mean_temp:tail(names(.), 1)),
               values_to = "value", 
               names_to = "predictor") %>%  
  group_by(sp_name, predictor) %>% 
  summarise(correlation = cor.test(ctmax, value)$estimate,
            p.value = cor.test(ctmax, value)$p.value,
            ci_low = cor.test(ctmax, value)$conf.int[1],
            ci_high = cor.test(ctmax, value)$conf.int[2]) %>% 
  mutate(sig = ifelse(p.value <0.05, "Sig.", "Non Sig."))

corr_vals %>%  
  filter(sig == "Sig.") %>% 
  drop_na(correlation) %>% 
  group_by(sp_name) %>%
  arrange(desc(correlation)) %>% 
  slice_head(n = 3) %>% 
  select("Species" = sp_name, "Predictor" = predictor, "Correlation" = correlation, "P-Value" = p.value) %>% 
  knitr::kable(align = "c")
```

## Trait Variation 
```{r ctmax-and-size-sum-plot, fig.width=20, fig.height=5}
ctmax_plot = full_data %>% 
  mutate( #sp_name = str_replace(sp_name, pattern = " ",
    #                              replacement = "\n"),
    sp_name = fct_reorder(sp_name, ctmax, mean)) %>% 
  ggplot(aes(y = sp_name, x = ctmax)) + 
  geom_point(aes(colour= sp_name_sub),
             position = position_dodge(width = 0.3),
             size = 4) + 
  scale_colour_manual(values = species_cols) + 
  xlab(NULL) + 
  labs(y = "",
       x = "CTmax (°C)",
       colour = "Group") + 
  theme_matt() + 
  theme(legend.position = "none")

size_plot = full_data %>% 
  mutate(sp_name = fct_reorder(sp_name, ctmax, mean)) %>% 
  ggplot(aes(y = sp_name, x = size)) + 
  geom_point(aes(colour= sp_name_sub),
             position = position_dodge(width = 0.3),
             size = 4) + 
  scale_colour_manual(values = species_cols) + 
  labs(x = "Prosome Length (mm)",
       y = "", 
       colour = "Group") + 
  guides(color = guide_legend(ncol = 1)) +
  theme_matt(base_size = ) + 
  theme(legend.position = "right",
        axis.text.y = element_blank(),
        plot.margin = margin(0, 0, 0, 0,"cm"))

trait_plot = ctmax_plot + size_plot
trait_plot
```

```{r ctmax-and-size-histograms, fig.width=7, fig.height=21, include = F}
ggplot(full_data, aes(x = size, fill = sp_name_sub)) + 
  facet_wrap(.~sp_name_sub, ncol = 1) + 
  geom_histogram(binwidth = 0.05) + 
  scale_fill_manual(values = species_cols) + 
  labs(x = "Prosome length (mm)") + 
  theme_matt_facets() + 
  theme(legend.position = "none")

ggplot(full_data, aes(x = ctmax, fill = sp_name_sub)) + 
  facet_wrap(.~sp_name_sub, ncol = 1) + 
  geom_histogram(binwidth = 1) + 
  scale_fill_manual(values = species_cols) + 
  labs(x = "CTmax (°C)") + 
  theme_matt_facets() + 
  theme(legend.position = "none")
```

```{r fecundity-histogram, fig.width=7, fig.height=10}
full_data %>%  
  drop_na(fecundity) %>%  
  ggplot(aes(x = fecundity, fill = sp_name_sub)) + 
  facet_wrap(.~sp_name_sub, ncol = 1) + 
  geom_histogram(binwidth = 2) + 
  scale_fill_manual(values = species_cols) + 
  labs(x = "Fecundity (# Eggs)") +
  theme_matt_facets() + 
  theme(legend.position = "none")
```

### Variation with temperature 

```{r trait-coll-temp-plots, fig.width=15, fig.height=10}
ctmax_temp = ggplot(full_data, aes(x = collection_temp, y = ctmax, colour = sp_name)) + 
  geom_smooth(method = "lm", linewidth = 3) +
  geom_point(size = 3) + 
  labs(x = "Collection Temperature (°C)", 
       y = "CTmax (°C)",
       colour = "Species") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

size_temp = ggplot(filter(full_data, sex != "juvenile"), aes(x = collection_temp, y = size, colour = sp_name)) + 
  geom_smooth(method = "lm", linewidth = 3) +
  geom_point(size = 3) + 
  labs(x = "Collection Temperature (°C)", 
       y = "Length (mm)",
       colour = "Species")  + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

wt_temp = ggplot(full_data, aes(x = collection_temp, y = warming_tol, colour = sp_name)) + 
  geom_smooth(method = "lm", linewidth = 3) +
  geom_point(size = 3) + 
  labs(x = "Collection Temperature (°C)", 
       y = "Warming Tolerance (°C)",
       colour = "Species")  + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

eggs_temp = ggplot(full_data, aes(x = collection_temp, y = fecundity, colour = sp_name)) + 
  geom_smooth(method = "lm", linewidth = 3) +
  geom_point(size = 3) + 
  labs(x = "Collection Temperature (°C)", 
       y = "Fecundity (# Eggs)",
       colour = "Species")  + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ggarrange(ctmax_temp, size_temp, wt_temp, eggs_temp, 
          common.legend = T, legend = "right")
```

```{r ctmax-coll-temp-model}
ctmax_temp.model = lm(data = full_data, ctmax ~ collection_temp * sp_name)

knitr::kable(car::Anova(ctmax_temp.model))

ctmax_resids = cbind(full_data, "resids" = ctmax_temp.model$residuals)
```

```{r ctmax-time-in-lab, fig.width=15, fig.height=10}
ggplot(ctmax_resids, aes(x = days_in_lab, y = resids, colour = sp_name)) + 
  facet_wrap(sp_name~.) + 
  geom_point(size = 4) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  scale_x_continuous(breaks = c(0:3)) + 
  labs(x = "Days in lab", 
       y = "CTmax Residuals") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt_facets() + 
  theme(legend.position = "none")
```

Given the long generation times of these copepods, decreases in trait variance may indicate selection over the seasonal cycle. Shown below are the variance in observed CTmax and size, plotted against collection date. Variance decreases in *Skistodiaptomus*, but this pattern is driven by a single collection with high variance early in the year. Size variance increases slightly in *Skistodiaptomus*. Variance in both CTmax and size is fairly constant in *Leptodiaptomus minutus*, the only other species collected across the entire set of samples thus far. 

```{r trait-variance-coll-temp}
ggplot(drop_na(species_summaries, ctmax_var), aes(x = as.Date(collection_date), y = ctmax_var, colour = sp_name)) + 
  facet_wrap(sp_name~.) + 
  geom_point(size = 2) + 
  geom_smooth(method = "lm", se = F) + 
  labs(x = "Collection Temp. (°C)", 
       y = "CTmax Variance") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt_facets() + 
  theme(legend.position = "none")

ggplot(drop_na(species_summaries, size_var), aes(x = as.Date(collection_date), y = size_var, colour = sp_name)) + 
  facet_wrap(sp_name~.) + 
  geom_point(size = 2) + 
  geom_smooth(method = "lm", se = F) + 
  labs(x = "Collection Temp. (°C)", 
       y = "Size Variance") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt_facets() + 
  theme(legend.position = "none")
```


## Sex and stage variation in thermal limits 
Previous sections have generally lumped juvenile, female, and male individuals together. There may be important stage- or sex-specific differences in CTmax though. For several species, we have measurements for individuals in different stages or of different sexes. 

```{r sex-stage-table}
sex_sample_sizes = ctmax_resids %>%  
  group_by(sp_name, sex) %>%  
  summarise(num = n()) %>%  
  pivot_wider(id_cols = sp_name,
              names_from = sex, 
              values_from = num,
              values_fill = 0) %>% 
  select("Species" = sp_name, "Juvenile" = juvenile, "Female" = female, "Male" = male)

knitr::kable(sex_sample_sizes, align = "c")
```

The female-male and female-juvenile comparisons show that there are generally no differences in thermal limits between these groups. 

```{r ctmax-sex, fig.width=7, fig.height=7}
ctmax_resids %>% 
  filter(sp_name %in% filter(sex_sample_sizes, Male > 0, Female > 0)$Species & 
           sex != "juvenile") %>% 
  ggplot(aes(x = sex, y = resids, colour = sp_name, group = sp_name)) + 
  facet_wrap(sp_name~., ncol = 2) + 
  geom_smooth(method = "lm", se = F, linewidth = 1) + 
  geom_point(size = 3,
             alpha = 0.5,
             position = position_jitter(height = 0, width = 0.05)) +  
  labs(x = "Sex", 
       y = "CTmax Residuals") + 
  scale_colour_manual(values = species_cols) + 
  theme_bw(base_size = 18) + 
  theme(legend.position = "none", 
        panel.grid = element_blank())
```

```{r ctmax-stage, fig.width=3, fig.height=6}
ctmax_resids %>% 
  filter(sp_name %in% filter(sex_sample_sizes, Juvenile > 0 & Female > 0)$Species & 
           sex != "male") %>% 
  ggplot(aes(x = sex, y = resids, colour = sp_name, group = sp_name)) + 
  facet_wrap(sp_name~., ncol = 1) + 
  geom_smooth(method = "lm", se = F, linewidth = 1) + 
  geom_point(size = 3,
             alpha = 0.5,
             position = position_jitter(height = 0, width = 0.05)) +  
  labs(x = "Sex", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = species_cols) + 
  theme_bw(base_size = 18) + 
  theme(legend.position = "none", 
        panel.grid = element_blank())
```

## Trait Correlations 

```{r ctmax-size, fig.width=10, fig.height=7}
ggplot(full_data, aes(x = size, y = ctmax, colour = sp_name)) + 
  geom_smooth(data = full_data, 
              aes(x = size, y = ctmax),
              method = "lm", 
              colour ="black", 
              linewidth = 2.5) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  geom_point(size = 4) + 
  labs(x = "Length (mm)", 
       y = "CTmax (°C)",
       colour = "Species") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")
```

```{r ctmaxresids-size, fig.width=10, fig.height=7, include = F}
ggplot(ctmax_resids, aes(x = size, y = resids, colour = sp_name)) + 
  geom_smooth(data = ctmax_resids, 
              aes(x = size, y = resids),
              method = "lm", 
              colour ="black", 
              linewidth = 2.5) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  geom_point(size = 4) + 
  labs(x = "Length (mm)", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")
```


```{r fecundity-size, fig.width=10, fig.height=7}
ggplot(ctmax_resids, aes(x = size, y = fecundity, colour = sp_name)) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  geom_point(size = 4) + 
  labs(x = "Prosome length (mm)", 
       y = "Fecundity (# Eggs)",
       colour = "Species") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")
```

```{r, ctmax-fecundity, fig.width=10, fig.height=7}
ggplot(ctmax_resids, aes(x = ctmax, y = fecundity, colour = sp_name)) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  geom_point(size = 4) + 
  labs(x = "CTmax (°C)", 
       y = "Fecundity (# Eggs)") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")
```


